Objective:

To develop a deep learning-based diagnostic model for skin NTDs using a new dataset of skin images, specifically addressing challenges related to data scarcity and class imbalance.

Approach:

Key Findings:

• The study identifies transfer learning as the recommended deep learning strategy for skin NTDs diagnostics.
• A two-stage approach was designed to integrate feature mapping models and domain adaptation, which enhances model robustness by effectively addressing data scarcity and class imbalance.
• The developed model pipeline specifically addresses the challenges of data scarcity and class imbalance in skin NTDs.

Interpretation:

The findings establish a benchmark for deep learning methods tailored to address data scarcity issues in skin NTD diagnostics.

Limitations:

• The dataset used for model training was characterized by small-sized image samples, which limited the model's ability to generalize effectively.
• Challenges related to pretrained models include high data requirements and domain incompatibility, which may hinder the model's performance.

Conclusion:

The study provides a foundational benchmarking effort for developing deep learning diagnostic tools for skin NTDs, emphasizing the need for robust data management and model development strategies.

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.